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IET International radar Conference 2020, IET IRC 2020

作者： Guan, Yongsheng Ma, Lin Sun, Xumin Yan, Junkun Information Science Academy of China Electronics Technology Group Corporation Beijing China National laboratory of radar signal processing Xidian university Shanxi Xi'an China

ISBN: (纸本)9781839535635

In target tracking mission planning, one of the difficulties is to generate Multi-sensor mission planning under the guidance of resources information. This paper proposes a SVM-based target tracking multi-sensor mission planning method. The method firstly generates a mission-planning database as a training set based on typical mission scenarios. Then, the hierarchical learning model was established by SVM algorithm, and the sensor-target allocation and sensor resource allocation were carried out respectively. After the model was trained by the scheme database, the intelligent generation of mission planning scheme can be realized. So that the feasibility and robustness of the method are verified by simulation. © 2020 Institution of Engineering and technology. All rights reserved.

关键词： Target tracking

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A Fractional-Order Logarithmic Adaptive Filtering Algorithm Under $\alpha$-Stable Noise

A Fractional-Order Logarithmic Adaptive Filtering Algorithm ...

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IEEE International Conference on Electronic Information and Communication technology (ICEICT)

作者： Dazhi Sun Canping Yu Li Li Yingsong Li Wudang Xiao Yong Chen Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China Aircraft Airworthiness Institute China Academy of Civil Aviation Science and Technology (Center of Aviation Safety Technology CAST) Beijing China

In practical applications, noise is more appropriately modelled as an a-stable random process than as a Gaussian process. To cope with a-stable noise, a fractional-order stochastic gradient descent (SGD) method is invented to adaptive filtering (AF) algorithms. When both input signal and noise follow α-stable random processes, the AF algorithms based on mean square error (MSE) criteria can be affected. Nevertheless, the fractional-order AF (FoAF) algorithm can effectively address this issue. For the purpose of better stability performance of the FoAF algorithm, different cost functions (CFs) are introduced into fractional-order SGD method. Based on the fractional-order derivative of a logarithmic CF (LCF), a fractional-order logarithmic adaptive filtering (FoLAF) algorithm is formed in this condition. Trials show that the FoLAF algorithm achieves faster convergence, excellent steady-state performance and superior tracking performance.

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Rate Maximization in Sweeping Robot-Assisted Reconfigurable Intelligent Surface Communication Systems

Rate Maximization in Sweeping Robot-Assisted Reconfigurable ...

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IEEE International Conference on Communications in China Workshops (ICCC)

作者： Zhongyu Wang Gengxin Zhao Yashuai Cao Jiawei Zhang Zheng Chang Hebei Key Laboratory of Information Transmission and Signal Processing School of Information Science and Engineering Yanshan University Qinhuangdao China Department of Electronic Engineering Tsinghua University Beijing China School of Computer Science University of Electronic Science and Technology of China Chengdu China Faculty of Information Technology University of Jyäaskylä Jyväskylä Finland

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

Reconfigurable intelligent surface (RIS) assisted communication is recognized as a promising technology in academia and industry because it offers a novel and promising solution for implementing both secure and good-quality wireless communication networks. The paper proposes installing an RIS on a sweeping robot to act as the mobile passive relay, which can flexibly deploy the location of RIS and enhance communication capacity. In this paper, the maximization problem of user throughput is formulated, and we jointly optimize transmission power at AP and reflection phase shifts at RIS. It is difficult to deal with the optimization problem. Therefore, we separate the two variables into two subproblems, i.e., the transmit power optimization problem and the reflection phase-shift optimization problem. We can solve these problems by semidefinite relaxation (SDR) and alternating optimization algorithms. The simulation results demonstrate the advantages of the proposed algorithm, which enhances the communication capacity efficiently.

关键词： Wireless communication Industries Service robots Simulation Conferences Reconfigurable intelligent surfaces Throughput Reflection Relays Optimization

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Inter-pulse carrier frequency agile design for cognitive radar

Inter-pulse carrier frequency agile design for cognitive rad...

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IET International radar Conference (IRC 2023)

作者： Yin Li Tao Fan Xianxiang Yu Lan Lan Huai Ding Guolong Cui School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu People's Republic of China National Key Laboratory of Radar Signal Processing Xidian University Xi'an People's Republic of China The 29th Research Institute of China Electronics Technology Group Corporation Chengdu People's Republic of China

ISBN: (数字)9781837240982

This paper deals with the joint design of inter-pulse frequency and receive filter to reduce the Doppler sidelobe for cognitive Frequency Agile radar (FAR) working in search and confirmation modes. At first, we introduce an inter-pulse coherent processing method that integrates phase compensation with Weighted Non-Uniform Discrete Fourier Transform (W-NUDFT). Then, a multi-parameter non-convex optimization model is established based on the issue of high Doppler sidelobes caused by inter-pulse non-uniform accumulation. Subsequently, we develop an alternating iterative optimization algorithm that combines Coordinate Descent with Alternating Direction Method of Multipliers (CD-ADMM) to tackle the resulting non-convex optimization problem. Finally, through numerical simulations, the effectiveness of the waveform via the application on weak targets detection accompanied by strong targets is demonstrated.

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SAR Target Recognition Using Simulated Data by an Ensemble Multi-scale Deep Domain Adaptation Recognition Framework

SAR Target Recognition Using Simulated Data by an Ensemble M...

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IEEE International Conference on radar

作者： Yuanshuang Sun Yinghua Wang Liping Hu Hongwei Liu National Laboratory of Radar Signal Processing Xidian University Xi'an China Science and Technology on Electromagnetic Scattering Laboratory Beijing Institute of Environmental Features Beijing China

ISBN: (纸本)9781665468893

Obtaining lots of synthetic aperture radar (SAR) data for automatic target recognition (ATR) can be unrealistic in many situations. Relatively speaking, electromagnetic simulated SAR data is easier to acquire. However, there is an apparent distribution gap between the electromagnetic simulated data and the real data. It is not suitable to use the simulated data for recognition directly, and thus the way to apply the simulated SAR data for recognition is worth considering. In this paper, we focus on a challenging recognition problem of training solely on simulated data and testing on real data. Firstly, a multi-scale feature extraction module is used for robust feature extraction, which aims to make the extracted feature more generalized for both simulated data and real data. Secondly, the idea of domain adaptation is considered to learn a representation trained to jointly optimize for classification and domain invariance. Thirdly, an ensembling technique is applied, and an ensemble multi-scale deep domain adaptation recognition framework is proposed. The Moving and Stationary Target Acquisition and Recognition (MSTAR) benchmark data set and an electromagnetic simulated SAR data set are used for verification, and experimental results illustrate the effectiveness of our methods.

关键词： Training Target recognition Benchmark testing Feature extraction Robustness Data mining Electromagnetics

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Value Identification in Multistakeholder Recommender Systems for Humanities and Historical Research: The Case of the Digital Archive ***

arXiv

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arXiv 2024年

作者： Atzenhofer-Baumgartner, Florian Geiger, Bernhard C. Vogeler, Georg Kowald, Dominik Institute of Interactive Systems and Data Science Graz University of Technology Graz Austria Department of Digital Humanities University of Graz Graz Austria Know Center Research GmbH Graz Austria Signal Processing and Speech Communication Laboratory Graz University of Technology Graz Austria

Recommender systems remain underutilized in humanities and historical research, despite their potential to enhance the discovery of cultural records. This paper offers an initial value identification of the multiple stakeholders that might be impacted by recommendations in ***, a digital archive for historical legal documents. Specifically, we discuss the diverse values and objectives of its stakeholders, such as editors, aggregators, platform owners, researchers, publishers, and funding agencies. These in-depth insights into the potentially conflicting values of stakeholder groups allow designing and adapting recommender systems to enhance their usefulness for humanities and historical research. Additionally, our findings will support deeper engagement with additional stakeholders to refine value models and evaluation metrics for recommender systems in the given domains. Our conclusions are embedded in and applicable to other digital archives and a broader cultural heritage context. © 2024, CC BY-SA.

关键词： Embedded systems

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SwinNet: Swin Transformer Drives Edge-Aware RGB-D and RGB-T Salient Object Detection

arXiv

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arXiv 2022年

作者： Liu, Zhengyi Tan, Yacheng He, Qian Xiao, Yun Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Anhui University Hefei China

Convolutional neural networks (CNNs) are good at extracting contexture features within certain receptive fields, while transformers can model the global long-range dependency features. By absorbing the advantage of transformer and the merit of CNN, Swin Transformer shows strong feature representation ability. Based on it, we propose a cross-modality fusion model, SwinNet, for RGB-D and RGB-T salient object detection. It is driven by Swin Transformer to extract the hierarchical features, boosted by attention mechanism to bridge the gap between two modalities, and guided by edge information to sharp the contour of salient object. To be specific, two-stream Swin Transformer encoder first extracts multi-modality features, and then spatial alignment and channel re-calibration module is presented to optimize intra-level cross-modality features. To clarify the fuzzy boundary, edge-guided decoder achieves inter-level cross-modality fusion under the guidance of edge features. The proposed model outperforms the state-of-the-art models on RGB-D and RGB-T datasets, showing that it provides more insight into the cross-modality complementarity ***://***/liuzywen/SwinNet © 2022, CC0.

关键词： Convolutional neural networks

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Unknown Type Streaming Feature Selection via Maximal Information Coefficient

Unknown Type Streaming Feature Selection via Maximal Informa...

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Peng Zhou Yunyun Zhang Yuanting Yan Shu Zhao Key Laboratory of Intelligent Computing and Signal Processing (the Ministry of Education of China) School of Computer Science and Technology Anhui University Hefei China

Feature selection aims to select an optimal minimal feature subset from the original datasets and has become an indispensable preprocessing component before data mining and machine learning, especially in the era of big data. Most feature selection methods implicitly assume that we can know the feature type (categorical, numerical, or mixed) before learning, then design corresponding measurements to calculate the correlation between features. However, in practical applications, features may be generated dynamically and arrive one by one over time, which we call streaming features. Most existing streaming feature selection methods assume that all dynamically generated features are the same type or assume we can know the feature type for each new arriving feature on the fly, but this is unreasonable and unrealistic. Therefore, this paper firstly studies a practical issue of Unknown Type Streaming Feature Selection and proposes a new method to handle it, named UT-SFS. Extensive experimental results indicate the effectiveness of our new method. UT-SFS is nonparametric and does not need to know the feature type before learning, which aligns with practical application needs.

关键词： Measurement Microwave integrated circuits Correlation Conferences Machine learning Big Data Feature extraction

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Target Spatial-Frequency Scattering Response Sparse Approximation using Multi-static radar Observations

Target Spatial-Frequency Scattering Response Sparse Approxim...

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IEEE International Conference on radar

作者： Shenghua Zhou Jiewen Cai Chaoying Huo Hui Ma Xiaojun Peng National Laboratory of Radar Signal Processing Xidian University P.R. China Science and Technology on Electromagnetic Scattering Laboratory P.R. China

ISBN: (纸本)9781665468893

Inverse synthetic aperture radar (ISAR) can form an image of the target with a sequence of wideband coherent observations, but it is generally difficult to generalize ISAR images from other directions. In this paper, with observations of a noncoherent multistatic radar system to the same target, we study how to approximate the target spatial-frequency joint response by the scattering centers target model. We optimize the scattering intensities of the scatterers such that the target response at selected frequency ticks and spatial directions, under sparse constraints to select proper number of scattering centers. Numerical results with a 3D plane model indicates that the target response can be approximated by a few scattering centers in a good accuracy, even for spatial directions in which the target is not probed before.

关键词： Solid modeling Three-dimensional displays Scattering radar scattering radar imaging Sparse representation Multistatic radar

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Mainlobe Interference Suppression Method Based on Coprime Array

Mainlobe Interference Suppression Method Based on Coprime Ar...

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IEEE International Conference on radar

作者： Mao Jiaqi Luo Feng Zhang Yawen Radar Department Xi'an Research Institute of Navigation Technology Xi'an China National Laboratory of Radar Signal Processing Xidian University Xi'an China

ISBN: (纸本)9781665468893

When combating multiple mainlobe interferences in space, the traditional interference suppression methods based on eigen-projection have the problems of model mismatch and mainlobe distortion. In order to solve the above problems, a mainlobe interference suppression method based on coprime array is proposed. In the proved method, the signal covariance matrix of the virtual uniform array is constructed according to the observation vector, and the angles and powers of all incident signals are estimated by Capon spectrum. Then, the reconstructed interference plus noise covariance matrix is used to construct the corresponding subspace, and the component of mainlobe interferences is suppressed by eigen-projection. Finally, the reconstructed sidelobe interference plus noise covariance matrix is used to calculate the adaptive weight vector to form an ideal beam pattern. Simulation results demonstrate the effectiveness and superiority of the proposed method.

关键词： Interference suppression Simulation radar Distortion Covariance matrices

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